Techniques are described for improving real-time application protection (RTAP) systems (e.g., web application firewalls (WAFs), runtime application self-protection (RASP) systems). In particular, a device within a trusted network may configured to predict vulnerabilities of proposed configurations for the RTAP systems. For example, the device may train one or more machine learning models with a first plurality of configuration settings of application protection systems corresponding to a plurality of applications and a first plurality of known vulnerabilities corresponding the first plurality of configuration settings; apply the one or more machine learning models to a proposed configuration setting to predict one or more potential vulnerabilities of the proposed configuration setting; and identify one or more configuration changes to the proposed configuration setting to overcome the predicted one or more potential vulnerabilities.
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5. The method of claim 1, wherein training each of the first machine learning model and the second machine learning model comprises training each of the first machine learning model and the second machine learning model to predict whether configuration data in a respective type of configuration file or a respective section of a configuration file is affected by a respective type of vulnerability of the plurality of known vulnerabilities.
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December 15, 2020
November 12, 2024
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